{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison in methods with gradient in the channel and without\n",
    "\n",
    "The purpose of this notebook is to compare the approaches presented in papers *An Introduction to Deep Learning for the Physical Layer* by [O'Shea and Hoydis, 2017](https://arxiv.org/pdf/1702.00832.pdf) and *End-to-End Learning of Communications Systems Without a Channel Model* by [Aoudia and Hoydis, 2018](https://arxiv.org/pdf/1804.02276.pdf). Both approaches propose Deep Neural Networks to learn End-to-end communication systems. The difference between them is that the work by O'Shea and Hoydis relies that we know the gradients of the channel while the one by Aoudia and Hoydis doesn't. Instead Aoudia and Hoydis approach has a feedback from the receiver to the transmitter and uses a policy converting it in a Reinforcement Learning task.\n",
    "\n",
    "For the implementation of both approaches we are going to use Pytorch. First making all the necessary imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "# To make the plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# To supress some strange output at runtime\n",
    "from IPython.utils import io\n",
    "# Used during plotting the training loss\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If possible we are going to use GPU to train faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print('Pytorch device: %s' % (device))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The models implemented in Pytorch and other necessary utilities are loaded from different files. This is done to have a single point of modification and the modules can be used in different places. It also helps us to keep this notebook as clean as possible and focus on the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import Transmitter, Receiver, Policy, Encoder, Decoder\n",
    "from utils import plot_constellation, count_errors, MemoryMessages, recover_models\n",
    "from comms_utils import channel, block_encoder, block_decoder, bler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the pretrained models are located at this folder. Also if we use this notebook to train a new model is going to be saved at this folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODELS_FOLDER = 'trained_models'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a flag tha indicates if the notebook should train the models. If the flag is `False` then it is assumed we have some pre-trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised approach. \n",
    "\n",
    "This will be the training loop for the first approach defined in *An Introduction to Deep Learning for the Physical Layer*. It is called supervised because it can be reduced to an Autoencoder with a classification task at the end. We know what goes in into the encoder so we just have to *classify* what comes out of the decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_supervised(m, n, snr_db=10, chann_type=\"AWGN\", batch_size=32, stop_value=0.005,\n",
    "                     n_epochs=1000, lr=0.001, plot=None):\n",
    "    # Get k. Number of bits necessary to transmit the m messages\n",
    "    k = math.log2(m)\n",
    "    \n",
    "    # Initialize the encoder and decoder\n",
    "    encoder = Encoder(m=m, n=n, use_embedding=True, use_paper_norm=False)\n",
    "    encoder.to(device)\n",
    "    decoder = Decoder(m=m, n=n)\n",
    "    decoder.to(device)\n",
    "    \n",
    "    # Adam optimizer for both encoder and decoder\n",
    "    enc_optimizer = torch.optim.Adam(encoder.parameters(), lr=lr)\n",
    "    dec_optimizer = torch.optim.Adam(decoder.parameters(), lr=lr)\n",
    "\n",
    "    # Variables to keep track of losses during training\n",
    "    losses = []\n",
    "    avg_losses = []\n",
    "    errors = []\n",
    "    avg_errors = []\n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        epoch_errors = 0\n",
    "        # Beginning of the epoch. Fill out the memory\n",
    "        messages = MemoryMessages(m)\n",
    "\n",
    "        # Until we have something in the memory the epoch has not finished\n",
    "        while len(messages) > 0:\n",
    "            # Sample from the memory that is left\n",
    "            batch, _ = messages.sample(batch_size)\n",
    "            batch_len = len(batch)\n",
    "\n",
    "            # Make sure the gradients are zero\n",
    "            enc_optimizer.zero_grad()\n",
    "            dec_optimizer.zero_grad()\n",
    "\n",
    "            # If we are using embedding the input must be an integer with the id of the message to transmit\n",
    "            # e.g. [0, 1, 2, 3, ...]\n",
    "            data = torch.from_numpy(batch).unsqueeze(1).to(device)\n",
    "\n",
    "            # Encode the data\n",
    "            encoded_data = encoder(data)\n",
    "\n",
    "            # Pass the encoded message through the channel\n",
    "            data_channel = channel(encoded_data, n, k, snr_db, chann_type=chann_type)\n",
    "\n",
    "            # Decode the message\n",
    "            decoded_data = decoder(data_channel)\n",
    "\n",
    "            # Check if the received labels are the same as the originals\n",
    "            targets = torch.from_numpy(batch).to(device)\n",
    "            loss = F.nll_loss(decoded_data, targets)\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "            # Get how many classifications mistakes we made\n",
    "            epoch_errors += count_errors(decoded_data, targets)\n",
    "\n",
    "            enc_optimizer.step()\n",
    "            dec_optimizer.step()\n",
    "            \n",
    "            losses.append(loss.detach().to(\"cpu\").numpy())\n",
    "            errors.append(epoch_errors/m)\n",
    "            \n",
    "            last_losses = np.array(losses[-100:])\n",
    "            # If the loss is small enough\n",
    "            if np.all(last_losses < stop_value):\n",
    "                return encoder, decoder, errors\n",
    "\n",
    "        # If we desire to visualize the training we enter this if\n",
    "        if plot is not None:\n",
    "            print(\"Finished epoch: %d. Errors %f. Loss: %f\" % (epoch, errors[-1], losses[-1]), end=\"\\r\")\n",
    "            \n",
    "            if epoch > plot:\n",
    "                avg = np.mean(losses[-plot:])\n",
    "                avg_err = np.mean(errors[-plot:])\n",
    "            else:\n",
    "                avg = np.mean(losses)\n",
    "                avg_err = np.mean(errors)\n",
    "            avg_losses.append(avg)\n",
    "            avg_errors.append(avg_err)\n",
    "        \n",
    "            # Make a plot of training\n",
    "            if epoch%plot==0 and epoch != 0:\n",
    "                plt.clf()\n",
    "    #             plt.plot(losses, label=\"Loss\")\n",
    "    #             plt.plot(avg_losses, label=\"Average loss\")\n",
    "                plt.plot(errors, label=\"Errors. Supervised training\")\n",
    "                plt.plot(avg_errors, label=\"Average Errors. Supervised training\")\n",
    "                plt.legend(loc='upper right')\n",
    "                display.clear_output(wait=True)\n",
    "                display.display(plt.gcf())\n",
    "            \n",
    "            if epoch == n_epochs-1:\n",
    "                print()\n",
    "                print(\"Finished training. Errors %f. Loss: %f\" % (errors[-1], losses[-1]))\n",
    "    \n",
    "    return encoder, decoder, errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternated approach\n",
    "\n",
    "Next we are going to define the training loop for the approach in *End-to-End Learning of Communications Systems Without a Channel Model*. The training loop can be divided in 2 major phases:\n",
    "\n",
    "- Training of the receiver: Can be seen as a supervised learning task\n",
    "- Training of the transmitter: RL-based training based on an estimated gradient of the loss\n",
    "\n",
    "This is why from where on forward we are going to refer to this approach as **Alternated**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_alternated(m=256, n=4, snr_db=10, sigma_var=0.02, chann_type=\"AWGN\",\n",
    "                    n_epochs=1000, lr=0.001, batch_size=32, plot=False, stop_value=0.005):\n",
    "    # Get k. Number of bits necessary to transmit the m messages\n",
    "    k = math.log2(m)\n",
    "    \n",
    "    # Initialize the transmitter, receiver and policy\n",
    "    tx = Transmitter(m=m, n=n)\n",
    "    tx.to(device)\n",
    "    rx = Receiver(m=m, n=n)\n",
    "    rx.to(device)\n",
    "    policy = Policy(m=m, n=n, sigma_var=sigma_var)\n",
    "    policy.to(device)\n",
    "    \n",
    "    # Adam optimizer for both rx and tx\n",
    "    tx_optimizer = torch.optim.Adam(tx.parameters(), lr=lr)\n",
    "    rx_optimizer = torch.optim.Adam(rx.parameters(), lr=lr)\n",
    "\n",
    "    # Variables to keep track of training\n",
    "    losses_tx = []\n",
    "    losses_rx = []\n",
    "    avg_losses_tx = []\n",
    "    avg_losses_rx = []\n",
    "    errors_rx = []\n",
    "    errors_tx = []\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        # Beginning of the epoch. Fill out the memory\n",
    "        messages = MemoryMessages(m)\n",
    "        error_rx = 0\n",
    "        error_tx = 0\n",
    "        \n",
    "        # Until we have something in the memory the epoch has not finished\n",
    "        while len(messages) > 0:\n",
    "            # Sample from the memory that is left\n",
    "            batch, _ = messages.sample(batch_size)\n",
    "            batch_len = len(batch)\n",
    "\n",
    "            # ** Phase 1 of alternated training - Train receiver **\n",
    "            rx_optimizer.zero_grad()\n",
    "            \n",
    "            # To ensure we are not using gradients from the channel\n",
    "            with torch.no_grad():\n",
    "                inputs_rx = torch.from_numpy(batch).unsqueeze(1).to(device)\n",
    "\n",
    "                tx.eval()\n",
    "                x_encoded = tx(inputs_rx)\n",
    "                tx.train()\n",
    "\n",
    "                x_channel = channel(x=x_encoded, n=n, k=k, snr_db=snr_db, chann_type=chann_type)\n",
    "            \n",
    "            # Get only gradients from the receiver\n",
    "            y = rx(x_channel, chann_type=chann_type)\n",
    "            \n",
    "            # Classification task. Supervised training. Check if what we received is correct\n",
    "            targets = torch.from_numpy(batch).to(device)\n",
    "            loss_rx = F.nll_loss(y, targets)\n",
    "            loss_rx.backward()\n",
    "\n",
    "            # Count the errors that we had in this phase\n",
    "            error_rx += count_errors(y, targets)\n",
    "            rx_optimizer.step()\n",
    "        \n",
    "            # ** Phase 2 of alternated training - Train transmitter **\n",
    "            tx_optimizer.zero_grad()\n",
    "            \n",
    "            inputs_tx = torch.from_numpy(batch).unsqueeze(1).to(device)\n",
    "            \n",
    "            # Get gradients from the transmitter and the policy\n",
    "            x_encoded = tx(inputs_tx)\n",
    "            xp, xp_logprob = policy(x_encoded)\n",
    "            \n",
    "            # To ensure we are not using gradients from the channel\n",
    "            with torch.no_grad():\n",
    "                xp_channel = channel(x=xp, n=n, k=k, snr_db=snr_db, chann_type=chann_type)\n",
    "\n",
    "                rx.eval()\n",
    "                y = rx(xp_channel, chann_type=chann_type)\n",
    "                rx.train()\n",
    "                \n",
    "                targets = torch.from_numpy(batch).to(device)\n",
    "                per_example_losses = F.nll_loss(y, targets, reduction='none')\n",
    "            \n",
    "            # With the per example losses calculated at the receiver make the loss of the transmitter\n",
    "            # NB the per example losses do not have a gradient\n",
    "            loss_tx = torch.mean(per_example_losses.detach().unsqueeze(1)*xp_logprob.reshape(batch_len, 2*n))\n",
    "            loss_tx.backward()\n",
    "            \n",
    "            # Count the errors that we had in this phase\n",
    "            error_tx += count_errors(y, targets)\n",
    "            \n",
    "            tx_optimizer.step()\n",
    "        \n",
    "        losses_rx.append(loss_rx.detach().to(\"cpu\").numpy())\n",
    "        losses_tx.append(loss_tx.detach().to(\"cpu\").numpy())\n",
    "        errors_rx.append(error_rx/m)\n",
    "        errors_tx.append(error_tx/m)\n",
    "        \n",
    "        # If the loss is small enough\n",
    "        last_losses = np.array(losses_rx[-100:])\n",
    "        if np.all(last_losses < stop_value):\n",
    "            return tx, rx, errors_rx, errors_tx\n",
    "            \n",
    "        # If we desire to visualize the training we enter this if\n",
    "        if plot is not None:\n",
    "            print(\"Finished epoch: %d. Error rx: %f. Error tx: %f. Loss rx: %f. Loss tx: %f\" % (epoch,\n",
    "                                                                                                errors_rx[-1], errors_tx[-1],\n",
    "                                                                                                losses_rx[-1], losses_tx[-1]), end=\"\\r\")\n",
    "\n",
    "            if epoch > plot:\n",
    "                avg_rx = np.mean(losses_rx[-plot:])\n",
    "                avg_tx = np.mean(losses_tx[-plot:])\n",
    "            else:\n",
    "                avg_rx = np.mean(losses_rx)\n",
    "                avg_tx = np.mean(losses_tx)\n",
    "            avg_losses_rx.append(avg_rx)\n",
    "            avg_losses_tx.append(avg_tx)\n",
    "\n",
    "            # Make a plot of training\n",
    "            if epoch%plot==0 and epoch != 0:\n",
    "                plt.clf()\n",
    "                plt.plot(errors_rx, label=\"Rx errors. Alternating training\")\n",
    "                plt.plot(errors_tx, label=\"Tx errors. Alternating training\")\n",
    "                plt.legend(loc='upper right')\n",
    "                display.clear_output(wait=True)\n",
    "                display.display(plt.gcf())\n",
    "            \n",
    "            if epoch == n_epochs-1:\n",
    "                print()\n",
    "                print(\"Finished training. Error rx: %f. Error tx: %f. Loss rx: %f. Loss tx: %f\" % (errors_rx[-1], errors_tx[-1],\n",
    "                                                                                                   losses_rx[-1], losses_tx[-1]))\n",
    "\n",
    "\n",
    "    return tx, rx, errors_rx, errors_tx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are going to train and see the results with the parameters proposed in *End-to-End Learning of Communications Systems Without a Channel Model*. $(M, n) = (256, 4)$. In a traditional communications notation it is equivalent to $(n, k) = (4, 8)$. This means that the purpose of the trained system is to use 4 bits to represent messages that would need at least 8 bits to be represented. Normally in communcations we try to do the opposite, use more bits $n > k$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the parameters in the paper End-to-End Learning of Communications Systems Without a Channel Model\n",
    "# Train both models with them\n",
    "m = 256\n",
    "n = 4\n",
    "snr_db = 10\n",
    "chann_type = \"AWGN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished epoch: 199. Error rx: 0.007812. Error tx: 0.019531. Loss rx: 0.054333. Loss tx: -39.6122744\n",
      "Finished training. Error rx: 0.007812. Error tx: 0.019531. Loss rx: 0.054333. Loss tx: -39.612274\n",
      "Models saved in trained_models/AWGN_256_4_tx.pth, trained_models/AWGN_256_4_rx.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train:\n",
    "    # Training loop\n",
    "    tx, rx, errors_rx, errors_tx = train_alternated(m=m, n=n, snr_db=snr_db, chann_type=chann_type, lr=0.001,\n",
    "                                                   batch_size=32, n_epochs=200, plot=10)\n",
    "\n",
    "\n",
    "    # Save the trained models\n",
    "    tx_filename = \"%s/%s_%d_%d_tx.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "    rx_filename = \"%s/%s_%d_%d_rx.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "    torch.save(tx.state_dict(), tx_filename)\n",
    "    torch.save(rx.state_dict(), rx_filename)\n",
    "\n",
    "    print(\"Models saved in %s, %s\" % (tx_filename, rx_filename))\n",
    "    \n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished epoch: 3999. Errors 0.046875. Loss: 0.463021\n",
      "Finished training. Errors 0.046875. Loss: 0.463021\n",
      "Models saved in trained_models/AWGN_256_4_encoder.pth, trained_models/AWGN_256_4_decoder.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train:\n",
    "    # Training loop\n",
    "    encoder, decoder, errors = train_supervised(m=m, n=n, snr_db=snr_db, chann_type=chann_type, batch_size=32,\n",
    "                                                n_epochs=4000, lr=0.001, plot=100)\n",
    "\n",
    "    # Save the trained models\n",
    "    enc_filename = \"%s/%s_%d_%d_encoder.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "    dec_filename = \"%s/%s_%d_%d_decoder.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "    torch.save(encoder.state_dict(), enc_filename)\n",
    "    torch.save(decoder.state_dict(), dec_filename)\n",
    "\n",
    "    print(\"Models saved in %s, %s\" % (enc_filename, dec_filename))\n",
    "    \n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the errors we had at training with both approaches\n",
    "if train:\n",
    "    # Normally we trained supervised approach for more epochs.\n",
    "    # This lines are to make them the same size\n",
    "    errors_alternating = (np.array(errors_tx) + np.array(errors_rx))/2\n",
    "    errors_supervised = errors\n",
    "    padd_needed = len(errors_supervised) - len(errors_alternating)\n",
    "    errors_alternating_pad = np.pad(errors_alternating, (0, padd_needed), 'constant', constant_values=0)\n",
    "\n",
    "    # Plot the errors\n",
    "    plt.figure(figsize=(9, 7))\n",
    "    plt.plot(errors_alternating_pad, label=\"Errors. Alternated training\")\n",
    "    plt.plot(errors_supervised, label=\"Errors. Supervised training\")\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.grid(True)\n",
    "    plt.title(\"Errors during training\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can watch the constellations generated by our Tx and our Encoder. This is done by getting the encoding for all possible messages and then adding 20 noise samples. So for every $m$ message of $M$ we will plot 20 different samples with noise. After adding the noise we will visualize the constellation using 2 dimensional t-SNE. If the encoder and Tx are trained properly we would be able to clearly distinguish between the groups of noisy samples of each $m$ message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observe the constellations that were generated with each approach\n",
    "# An embedding to 2 dimensions using t-SNE was used to plot this\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), facecolor='w')\n",
    "ax1 = plot_constellation(m=256, n=4, model=\"supervised\", ax=ax1, device=device)\n",
    "ax2 = plot_constellation(m=256, n=4, model=\"alternated\", ax=ax2, device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End-to-end communication\n",
    "\n",
    "We have now trained for both approaches. The next thing to do is to use the trained models to define the end-to-end communications. We use the pre-trained models to encode a block of messages. First the messages will go through the Tx/Encoder, then they will go through the channel that will add noise and at the end we will pass them through the Rx/Decoder. As mentioned before, the Rx will output the probability the received sample corresponds to each $m$ message. We are going to pick the one with highest probability. After this decission we are going to check how many mistakes we had i.e. calculate the Block Error Rate (BLER)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_communication(m, n, snr_db, n_blocks, chann_type=\"AWGN\", model=\"supervised\", verbose=False):\n",
    "    enc, dec = recover_models(device, model=model, m=m, n=n, chann_type=chann_type)\n",
    "    \n",
    "    # Print the parameters of our trained models\n",
    "    if verbose: print(\"Models\")\n",
    "    if verbose: print(enc)\n",
    "    if verbose: print(dec)\n",
    "    if verbose: print()\n",
    "\n",
    "    k = math.log2(m)\n",
    "    # We are not training hence not using gradients. Just evaluating\n",
    "    with torch.no_grad():\n",
    "        # Generate as much data as specified in the parameters\n",
    "        data = torch.randint(0, m, (n_blocks, 1)).to(device)\n",
    "        if verbose: print(\"Original x\")\n",
    "        if verbose: print(data)\n",
    "        \n",
    "        # Encode the data (Tx)\n",
    "        enc_data = enc(data)\n",
    "        if verbose: print(\"Encoded x\")\n",
    "        if verbose: print(enc_data)\n",
    "    \n",
    "        # Pass the data through the channel. Add noise\n",
    "        noise_data = channel(enc_data, n, k, snr_db, chann_type=chann_type)\n",
    "        if verbose: print(\"x with noise\")\n",
    "        if verbose: print(noise_data)\n",
    "        \n",
    "        # Decode the data (Rx)\n",
    "        dec_data = dec(noise_data, chann_type=chann_type)\n",
    "        \n",
    "        # The last layer returns probabilites over all possible messages\n",
    "        # Choos the one that has the highest one\n",
    "        data_dec = torch.argmax(dec_data, dim=1).unsqueeze(1)\n",
    "        if verbose: print(\"Recovered x\")\n",
    "        if verbose: print(data_dec)\n",
    "        \n",
    "        # Count the errors that we had\n",
    "        errors = data_dec != data\n",
    "        total_errors = errors.sum().to(\"cpu\").numpy()\n",
    "\n",
    "    # Get the error. Rate of the errrors\n",
    "    bler = total_errors/n_blocks\n",
    "    if verbose: print(\"BLER\")\n",
    "    if verbose: print(bler)\n",
    "\n",
    "    print(\"Finished calculations for channel (%s). SNR dB: %f.\" % (chann_type, snr_db))\n",
    "    return bler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models\n",
      "Encoder(\n",
      "  (linear_M): Sequential(\n",
      "    (0): Embedding(256, 256)\n",
      "    (1): ReLU()\n",
      "  )\n",
      "  (linear_N): Sequential(\n",
      "    (0): Linear(in_features=256, out_features=4, bias=True)\n",
      "  )\n",
      "  (normalization): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "Decoder(\n",
      "  (linear_relu): Sequential(\n",
      "    (0): Linear(in_features=4, out_features=256, bias=True)\n",
      "    (1): ReLU()\n",
      "  )\n",
      "  (linear_out): Sequential(\n",
      "    (0): Linear(in_features=256, out_features=256, bias=True)\n",
      "    (1): LogSoftmax()\n",
      "  )\n",
      ")\n",
      "\n",
      "Original x\n",
      "tensor([[153],\n",
      "        [155],\n",
      "        [ 49],\n",
      "        [189],\n",
      "        [ 43],\n",
      "        [176],\n",
      "        [136],\n",
      "        [208],\n",
      "        [ 59],\n",
      "        [ 39]], device='cuda:0')\n",
      "Encoded x\n",
      "tensor([[-3.1385, -1.4431,  1.2202, -1.7877],\n",
      "        [-1.0541, -1.0679,  4.1271,  0.7468],\n",
      "        [ 0.9141, -1.5453,  3.1390,  0.7368],\n",
      "        [ 0.0676,  1.9721,  3.2915, -3.2703],\n",
      "        [-2.7056,  1.4390, -3.4901, -0.8934],\n",
      "        [-1.0359,  0.4548,  1.5310, -1.7149],\n",
      "        [-0.1751, -1.8484, -0.2737,  0.8924],\n",
      "        [-1.5003,  1.7603, -0.6366,  3.4187],\n",
      "        [ 1.5549,  0.3901,  1.9429, -0.7563],\n",
      "        [-3.0466,  0.2624,  2.7277, -2.4329]], device='cuda:0')\n",
      "x with noise\n",
      "tensor([[-3.3748, -1.5718,  1.0363, -2.2738],\n",
      "        [-0.8839, -0.9513,  3.9904,  0.7966],\n",
      "        [ 0.9000, -2.4251,  2.0802,  0.1312],\n",
      "        [-0.2093,  2.7900,  4.1072, -3.3018],\n",
      "        [-3.5663,  1.1050, -2.5125, -0.5110],\n",
      "        [-1.5438,  0.7461,  1.3390, -2.1213],\n",
      "        [-0.1622, -1.8621,  0.0509,  0.2148],\n",
      "        [-2.1874,  2.1827, -0.3209,  3.8716],\n",
      "        [ 2.2155,  0.6909,  2.3729, -0.8846],\n",
      "        [-2.7049,  0.4016,  2.4468, -3.0536]], device='cuda:0')\n",
      "Recovered x\n",
      "tensor([[153],\n",
      "        [155],\n",
      "        [ 95],\n",
      "        [189],\n",
      "        [ 43],\n",
      "        [176],\n",
      "        [136],\n",
      "        [208],\n",
      "        [ 59],\n",
      "        [ 39]], device='cuda:0')\n",
      "BLER\n",
      "0.1\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n"
     ]
    }
   ],
   "source": [
    "# Visualize for the supervised approach\n",
    "nn_communication(m=256, n=4, snr_db=0, n_blocks=10, model=\"supervised\", chann_type=\"AWGN\", verbose=True)\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models\n",
      "Transmitter(\n",
      "  (transmit): Sequential(\n",
      "    (0): Embedding(256, 256)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=256, out_features=8, bias=True)\n",
      "  )\n",
      "  (normalization): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "Receiver(\n",
      "  (receive): Sequential(\n",
      "    (0): Linear(in_features=8, out_features=256, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
      "    (3): LogSoftmax()\n",
      "  )\n",
      "  (estimate_h): Sequential(\n",
      "    (0): Linear(in_features=8, out_features=20, bias=True)\n",
      "    (1): Tanh()\n",
      "    (2): Linear(in_features=20, out_features=2, bias=True)\n",
      "  )\n",
      ")\n",
      "\n",
      "Original x\n",
      "tensor([[153],\n",
      "        [130],\n",
      "        [ 96],\n",
      "        [228],\n",
      "        [165],\n",
      "        [ 78],\n",
      "        [124],\n",
      "        [207],\n",
      "        [ 41],\n",
      "        [140]], device='cuda:0')\n",
      "Encoded x\n",
      "tensor([[[-1.9814, -1.3131],\n",
      "         [ 0.0178,  0.7638],\n",
      "         [-0.8129, -1.5726],\n",
      "         [-2.4467,  0.9349]],\n",
      "\n",
      "        [[-1.0848,  0.3690],\n",
      "         [-2.2450, -0.2794],\n",
      "         [-1.8239, -1.3431],\n",
      "         [-0.2854,  0.0587]],\n",
      "\n",
      "        [[-1.1489, -1.2554],\n",
      "         [-1.4669, -0.7345],\n",
      "         [ 1.0060, -3.5255],\n",
      "         [ 0.9979, -0.2828]],\n",
      "\n",
      "        [[-2.3506, -1.3634],\n",
      "         [-0.3925,  0.9797],\n",
      "         [ 0.3494, -1.1300],\n",
      "         [ 1.6839,  2.6993]],\n",
      "\n",
      "        [[-1.2482,  0.4226],\n",
      "         [-1.0827, -0.4361],\n",
      "         [ 0.0291,  0.7935],\n",
      "         [-0.4243,  1.5700]],\n",
      "\n",
      "        [[-0.2558,  1.8591],\n",
      "         [-1.5332, -0.4956],\n",
      "         [-0.2606, -0.5126],\n",
      "         [ 0.3807,  1.2019]],\n",
      "\n",
      "        [[-1.0603, -0.7804],\n",
      "         [-3.1550,  0.3742],\n",
      "         [ 1.1954, -3.9883],\n",
      "         [ 1.3123,  1.3881]],\n",
      "\n",
      "        [[ 0.8905, -0.9320],\n",
      "         [-1.3472, -0.6984],\n",
      "         [-0.5551, -1.5368],\n",
      "         [ 0.2893,  1.3887]],\n",
      "\n",
      "        [[-0.4238, -1.1002],\n",
      "         [-1.7173, -2.3891],\n",
      "         [-1.0037, -2.1582],\n",
      "         [ 0.2164,  2.8575]],\n",
      "\n",
      "        [[ 0.9963,  1.0776],\n",
      "         [-1.0356, -0.7533],\n",
      "         [-1.1772, -0.5460],\n",
      "         [ 0.1411,  1.3181]]], device='cuda:0')\n",
      "x with noise\n",
      "tensor([[[-2.2779e+00, -1.2805e+00],\n",
      "         [-3.9096e-01,  2.6310e-01],\n",
      "         [-1.0952e+00, -1.4953e+00],\n",
      "         [-2.4670e+00,  1.5155e+00]],\n",
      "\n",
      "        [[-1.1437e+00,  1.3652e+00],\n",
      "         [-2.3218e+00,  3.1644e-01],\n",
      "         [-1.4261e+00, -1.4858e+00],\n",
      "         [-3.5765e-01,  2.2424e-01]],\n",
      "\n",
      "        [[-1.1927e+00, -7.2427e-02],\n",
      "         [-1.9386e+00, -1.0308e+00],\n",
      "         [ 1.6445e+00, -3.9370e+00],\n",
      "         [ 1.6910e+00, -9.9428e-01]],\n",
      "\n",
      "        [[-2.0653e+00, -1.0465e+00],\n",
      "         [-3.4914e-01,  1.2932e+00],\n",
      "         [ 1.9322e-01, -1.2988e+00],\n",
      "         [ 1.1986e+00,  2.5315e+00]],\n",
      "\n",
      "        [[-1.8331e+00,  1.4126e+00],\n",
      "         [-1.6814e+00, -2.0828e-01],\n",
      "         [-6.1358e-01,  6.9409e-01],\n",
      "         [-1.6707e-03,  6.6897e-01]],\n",
      "\n",
      "        [[-1.3503e-01,  1.6969e+00],\n",
      "         [-1.6903e+00, -8.0783e-01],\n",
      "         [ 3.0175e-01, -3.3864e-01],\n",
      "         [ 9.7554e-01,  1.7738e+00]],\n",
      "\n",
      "        [[-1.5723e+00, -6.9241e-01],\n",
      "         [-3.6331e+00,  1.3702e+00],\n",
      "         [ 9.0001e-01, -3.6726e+00],\n",
      "         [ 1.6279e+00,  1.8732e+00]],\n",
      "\n",
      "        [[ 1.3494e+00, -1.1476e+00],\n",
      "         [-1.2925e+00, -4.8749e-01],\n",
      "         [-5.5832e-01, -4.9260e-01],\n",
      "         [-1.2738e-01,  1.7734e+00]],\n",
      "\n",
      "        [[-8.0341e-01, -8.0332e-01],\n",
      "         [-1.5006e+00, -2.5173e+00],\n",
      "         [-1.1500e+00, -1.6170e+00],\n",
      "         [ 6.4623e-01,  2.1571e+00]],\n",
      "\n",
      "        [[ 3.7069e-01,  1.1239e+00],\n",
      "         [-7.2703e-01, -9.9706e-01],\n",
      "         [-1.1999e+00, -4.3489e-01],\n",
      "         [ 2.8938e-01,  1.4811e+00]]], device='cuda:0')\n",
      "Recovered x\n",
      "tensor([[153],\n",
      "        [ 33],\n",
      "        [126],\n",
      "        [228],\n",
      "        [  3],\n",
      "        [123],\n",
      "        [124],\n",
      "        [207],\n",
      "        [ 41],\n",
      "        [140]], device='cuda:0')\n",
      "BLER\n",
      "0.4\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize for the alternated approach\n",
    "nn_communication(m=256, n=4, snr_db=0, n_blocks=10, model=\"alternated\", chann_type=\"AWGN\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "We trained both approaches at the same noise level of $\\text{SNR}_{\\text{dB}}= 10$. Now we are going to evaluate their performance at different noise levels and observe the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 10.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 10.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 11.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 11.000000.\n"
     ]
    }
   ],
   "source": [
    "# Defining all noise levels\n",
    "snrs_db = np.arange(-4, 12, 1)\n",
    "\n",
    "# Number of input messages to simulate\n",
    "n_blocks = 200000\n",
    "\n",
    "# To store the resutls\n",
    "results_y = np.zeros((2, len(snrs_db)))\n",
    "\n",
    "# Iterate over all the noise levels\n",
    "for snr_n, snr_db in enumerate(snrs_db):\n",
    "    # Get the bler at each noise level\n",
    "    bler_supervised = nn_communication(m=256, n=4, snr_db=snr_db, n_blocks=n_blocks, model=\"supervised\", chann_type=\"AWGN\")\n",
    "    bler_alternated = nn_communication(m=256, n=4, snr_db=snr_db, n_blocks=n_blocks, model=\"alternated\", chann_type=\"AWGN\")\n",
    "    torch.cuda.empty_cache()\n",
    "    # Store the results\n",
    "    results_y[0, snr_n] = bler_supervised\n",
    "    results_y[1, snr_n] = bler_alternated\n",
    "    \n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "plt.figure(figsize=(9, 7))\n",
    "plt.semilogy(snrs_db, results_y[0, :], label=\"Supervised training\")\n",
    "plt.semilogy(snrs_db, results_y[1, :], label=\"Alternated training\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.title(\"Comparing approaches. Channel (m, n)=(256, 4) (n, k)=(4, 8)\")\n",
    "plt.xlabel('Eb/No (dB)')\n",
    "plt.ylabel('BLER')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More results\n",
    "\n",
    "Last results were obtained using an `M=256` and `n=4`. For representing 256 messages we would need at least 8 bits. This means that we are going to try to go from 8 bits to 4 bits. Normally in communications we do the opposite, we try to use more bits with the purpose of having *redundancy bits* and we can check from errors in the receiver side.\n",
    "\n",
    "Taking the last into account we are going to now use the proposed encodings in *An Introduction to Deep Learning for the Physical Layer* and compare both approaches with the theoretical results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (m, n) representation.\n",
    "# Equivalent to (n, k) = (4, 4), (7, 4), (8, 8), (2, 2)\n",
    "hamming_sizes = [(16, 4), (16, 7), (256, 8), (4, 2)]\n",
    "# Training at a constant SNR\n",
    "snr_db = 7\n",
    "chann_type = \"AWGN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models saved in trained_models/AWGN_4_2_tx.pth, trained_models/AWGN_4_2_rx.pth5. Loss tx: -0.5505772\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train:\n",
    "    for m, n in hamming_sizes:\n",
    "        print(\"Training alternated (%d, %d)\" % (m, n))\n",
    "        # Training loop\n",
    "        tx, rx, errors_rx, errors_tx = train_alternated(m=m, n=n, snr_db=snr_db, chann_type=chann_type, lr=0.001,\n",
    "                                                       batch_size=32, n_epochs=10000, plot=1000)\n",
    "\n",
    "        # Save the trained models\n",
    "        tx_filename = \"%s/%s_%d_%d_tx.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "        rx_filename = \"%s/%s_%d_%d_rx.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "        torch.save(tx.state_dict(), tx_filename)\n",
    "        torch.save(rx.state_dict(), rx_filename)\n",
    "\n",
    "        print(\"Models saved in %s, %s\" % (tx_filename, rx_filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bigger n values seem to converge faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models saved in trained_models/AWGN_4_2_encoder.pth, trained_models/AWGN_4_2_decoder.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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xY0cefvhhWrduXahMamoqycnJTJs2jVtvvZVPPvmE0aNH88c//pFp06aRmJjIpEmTyv05u0uv0JXPSE5Odl2Jjhw5kuTkZPz9/enfvz+LFi0iNzeXxYsXM3ToUNauXcvOnTvp2bMnMTExfPDBBxw4cMB1rILJYPv27fTq1YuoqCg++ugjduzYATiaKfKv/m+77TZX+WXLlrFs2TJiY2OJi4tj9+7drokkilqxYgVbtmxhy5YthR6MKW243z179hAREUGHDh0Ax4fAqlWrStwvOjqa22+/nQ8//LDcZpqQkBA2btzI1KlTCQ0NZcSIEW496DR06FDq1atHs2bNSEpKYv369WXWv23btiQkJAAQGhpKu3btWLt2LSdOnGD37t307Nmz0PG7devG+++/z+TJk/nhhx9o0KBBqb+73bt3ExERQWRkJCLC6NGjy40/35AhQ6hXrx4AOTk5jBs3jqioKIYPH87OnTtL3KdPnz6uYZU7d+5c6P2TLyIiwjUGzlVXXUV6ejonT54kKyuLxMREoPB7p6r0Cl15XjlX0tXhl19+Yfny5fzwww+ICHl5eYgIr7zyCiNHjuTNN9+kSZMmxMfH06BBA4wx3HjjjSQnJ5d4vILDv44dO5b58+fTtWtXZsyYwcqVK8uMxRjDE088UaXHzysz3G/RcosXL2bVqlUsWrSIF154gR9++KHMxO7n50fv3r3p3bs3UVFRfPDBB4wdOxZ/f3/sdjvg/nC/JdU/PT29WD1GjhzJnDlz6NSpEzfffHOx41177bWsWrWKxYsXM3bsWB577DEaN25c4u+uvOkHy1Iwrtdee43mzZuzdetW7HY7wcHBJe5TmeF+85tcqoteoSufMG/ePO644w4OHDhAeno6Bw8eJCIigtWrV3PdddexadMmpk2b5rqCT0hIYM2aNaSlpQFw9uzZUidvyMrKokWLFuTk5PDRRx+51ickJLhm3slvswfo168f7733nqtd99ChQ66haauqY8eOpKenu+KeOXNmiU0jdrudgwcPkpSUxMsvv8ypU6cKjepY1J49ewr9F7FlyxbXcL/h4eFs3LgRoNhMQwsWLODChQucOHGClStX0q1btwrV/+abb2bBggWF/rsq6MCBAzRv3pxx48Zxzz33sGnTplJ/d506dSI9PZ19+xwTw5f2Ye3OcL/50wfOnDmz0IxPntCoUSMaNGjg6kVU8L1TVZZL6KJTXKgSJCcnc/PNNxdad8stt5CcnIyfnx+DBw/m888/d90QDQ0NZcaMGYwaNYro6GgSExNL7er43HPP0aNHD3r27EmnTp1c6//1r3/x6quvEh0dTVpaGg0bNgQc7dG33XYbiYmJREVFMWzYsFITSFJSkqvb4p133lluPYODg3n//fcZPnw4UVFR2Gw27r///mLl8vLyGD16NFFRUcTGxjJ+/HgaNWpESkoK99xzT7HyZ86cYcyYMa6bqDt37mTy5MkAPPvss0yYMIH4+PhiPWeio6NJSkoiISGBp59+mpYtW1ao/o0bN+aKK67gwIEDdO/evdj2lStX0rVrV2JjY5k9ezYTJkwo9XcXHBzM1KlTGTRoEHFxcVx66aUlnjMpKYmdO3e6booW9cADD/DBBx/QtWtXdu/eXS2Te0+fPp1x48YRExPD2bNnXe+dqrLc8LnTVu3nhSW73C6vw+fWjLo4fO65c+eoV68eIsKsWbNITk5mwYIF3g6rxkyePLnQXKLKffnD/YJjfPwjR47w+uuvFytX0eFztQ1dqUrauHEjDz30EMYYGjVqxHvvveftkJRFLF68mBdffJHc3Fzatm3rsZE2NaErVUm9evUqdyo6X5bfJKMqbsSIESV2q6wqy7Whq9rLW813Svmiyvw9WS6hlzY4V2v5mettm2o2GOUSHBzMiRMnNKkr5QHGGE6cOFFql8nSWLLJxZ9cIuQoqSYMgAg5woqgPwOwy96GAdk13w+6rgsLCyMjI4PMzExvh6KUTwgODiYsLKxC+1gyoS8JfIIOtkNcd/FVDpjLXMkc4ArbT1xv28Rye5wXI6x7AgICiIiI8HYYStVplmtyAehgOwRAKznO722/jcp2wO7odxolP3olLqWU8iZLJvQc43i4oRmnSLA5+qTPyu3Nddn/AmCs/1KvxaaUUt5iySaXAHE8ivtG4FuudZNyx7leN5YzBJJDNgE1HptSSnmLJa/QS+bo/vJu7iAA+toq/hSqUkpZmSUTeqYpPO7BCdPA9XpmXl8AQsU7U0AppZS3WDKh77K3KbT8l5zfBifKMM04ahrTzbanpsNSSimvsmRCD5TC4w6vsMcWWBLsCAP91iPYazYwpZTyIrcSuoj0F5E9IpImIsXmSxKRNiKyQkQ2i8g2ESk++6oHBfBbQn80+0/Ftm+1Xw5AmOhDLkqpuqPchC4ifsBbwACgMzBKRDoXKfYUMMcYEwuMBP7j6UALKpjQ59t7Fts+zXljtINkVGcYSilVq7hzhd4dSDPG7DfGZAOzgKFFyhjgd87XDYHDnguxuPyE/k1eNKaEKqSZVgBEyqHqDEMppWoVdxJ6K+BggeUM57qCJgOjRSQDWAI8XNKBROReEUkRkZSqjPlxhe0gn+UlMCan5NmyT1OfI6YJkTa9QldK1R2euik6CphhjAkDBgIzRaTYsY0xU40x8caY+NDQ0EqdqOUvjnn4BvutLbPcXnsY0ToEgFKqDnEnoR8CWhdYDnOuK+huYA6AMeZ7IBho5okAi6p/8We3yv1gIoi0HaLJhYPlF1ZKKR/gTkLfAESKSISIBOK46bmwSJmfgD4AInIFjoReLV1McvxD3Cq3yR4JQPSvy6ojDKWUqnXKTejGmFzgIWApsAtHb5YdIjJFRIY4i/0ZGCciW4FkYKypppkOcv3quVVuuT2WU+YSGuQcr44wlFKq1nFrcC5jzBIcNzsLrnumwOudQPH+g14lbLVfTuS5nd4ORCmlaoTlnhQ1lDIHXQm2mMtpcT4NzupVulLK91kuoVOBhP5tXpTjxbbZ1RSLUkrVHpZL6Ka0WaJLsN5cwbHgcEh5r/oCUkqpWsKCE1y4n9ABFp/pyB8vLCV2UjK/uh5mdbi9Rxs+WvcTK//Sm/Bm9T0ZpFJK1TjLXaE7RhmAcdmPuVV6QZ7jXm0v2/Zi2z5a9xMAOw6f9lBsSinlPRZM6A4njXv90beZdpw1QYzwW1FqGUO19LBUSqkaZbmEbqtg93Y7NrabCNrbDoEmbqWUD7NcQs9PyhVJzYvyEmkuJ0sdH10q2C6vlFK1kYUTuvtJeHP+MACyv1oiUkqp2sByCT0/jVckoaeaVuQYP660pVdLTEopVRtYLqFXRjYB7DDhJNh2lbhdb4oqpXyB9RJ6Jcf8+s5+JVfZUgtNX6eUUr7EcgldKtGGDpBmbwlAvG2Px2NSSqnawHIJvTI3RQHW2LsA0El+8nhESilVG1guof92U7RifqYJmaYhneVACcfUbotKKeuzXEKvysNBP5rLGO6/qoQj6k1RpZT1WTChO1S0yQXguGkIwCVc8HQ4SinldZZL6JW9KQqwMO9qAC6Xwx6NSSmlagPLJfT8bouVaSTZadoCcJ1tqwcDUkqp2sFyCb0yT4rm+8lcCsBfAuaiA3UppXyN5RJ61RKxsCSvOwDhctQz4SilVC1huYQuroReua6Gb+cOAaCLpHsmIKWUqiUsl9DzVfY6fZdpw68mRNvRlVI+x3oJ3VS+lwtALv6st3fSIQCUUj7HegndqbIJHWCDvSMRtp8J5aQHI1JKKe+yXEIXD/ROSbF3BOAq294qH0sppWoLyyX0ykxBV9QOE855E0iPUsZHV0opK7JwQq98k0uOsx39Gtt2TwWllFJeZ8GE7lCVhA6wyh5FpO0QYZJZ2TkzlFKqVrFcQvfUQLfL7XEA9LZt8dARlVLKu9xK6CLSX0T2iEiaiEwqpcytIrJTRHaIyMeeDbPAeTzQhg6OoXR/sodynW0rosOhK6V8QLkJXUT8gLeAAUBnYJSIdC5SJhJ4AuhpjLkSeKQaYnWoYj/03wjr7Fdwo98mAi7+WvW4lFLKy9y5Qu8OpBlj9htjsoFZwNAiZcYBbxljfgUwxhzzbJi/8eTF9Hx7TwAuO7Lcg0dVSinvcCehtwIOFljOcK4rqAPQQUTWiMhaEelf0oFE5F4RSRGRlMzMzMpF7FT1K3THPKMZphnND2tCV0pZn6duivoDkUBvYBQwTUQaFS1kjJlqjIk3xsSHhoZW8lSe7JIifJl3FaHH1sB5fWpUKWVt7iT0Q0DrAsthznUFZQALjTE5xpgfgb04ErzHVWXGopJ8ktcLP/tF2PyhR46nlFLe4k5C3wBEikiEiAQCI4GFRcrMx3F1jog0w9EEs9+DcRbgmV4u+babCE40jYf1U9EO6UopKys3oRtjcoGHgKXALmCOMWaHiEwRkSHOYkuBEyKyE1gBPG6MOVEdAXv6Ch2En8KHwckDcHCdh46plFI1z9+dQsaYJcCSIuueKfDaAI85v2qE5xI6HGl5A7E/NIBN/4U2CR47rlJK1STLPSlaHVOB5vnXh85DYedCyDnv+RMopVQNsF5C93AbukvUMMjOgtRlnj6yUkrVCMsldM+3oTtFXAshzWHbHM8eVymlaojlEjpVnCS6VDY/6HKL4wpd+6QrpSzIggndoVp6GEYNg7xs2FW0V6ZSStV+lkvoUp19xVvGQZPL4Ye51XcOpZSqJtZL6NXVhg4gAlHD4cfVcPInzx9fKaWqkeUSer5qSegAsaMdiX3Tf6vn+EopVU0snNCrSaPWENnXkdDzcqrrLEop5XHWS+g1Md5K/F1w5mfYs6T8skopVUtYLqFXaxt6vvY3QMPWkPJe9Z1DKaU8zHIJnZpI6DY/uGoM7F8JJ/ZV33mUUsqDLJzQq1nsHWDzh43vV/eZlFLKIyyX0AN+WuN85bkr9IeTNxM+aTFzNhzktS/3Ej5pMUM/SONr+1Wc3zCTzT8eBeC9b39k5+HTHjuvUkp5kuUSur3d9azKi+JXQlzr7kxsS/fwJlU+9sRPtvH616kAbM04xQfZvamXc5Lp094EYMpnOxn4xuoqn0cppaqDW+Oh1ybd/jAB/jCBNDfKhk9aXKVzfWuP4kd7c+7y/xx4vkrHUkqp6ma5K/SaZMfGzLy+xNnS4Ocd3g5HKaXKpAm9HP/Lu4Zs4wdbPvZ2KEopVSZN6OU4SQNW2aNhx3wEu7fDUUqpUmlCd8OivEQ4nUE32ePtUJRSqlSa0N2wzB4PAZcwxO87b4eilFKl0oTuhvMEQ8cBDPBbjz+53g5HKaVKpAndXV1uoalkMdSmV+lKqdpJE7q72t8AwCC/tV4ORCmlSqYJ3V3+Qfw79yaus22FU4e8HY1SShWjCb0CZuf1drxIme7VOJRSqiSa0Csgw1zKcnssbP4Q8vTmqFKqdtGEXkGz85IcsxmlLvN2KEopVYgm9ApaYY+BkOaweaa3Q1FKqUI0oVdQHn7QdRTsXQpZR70djlJKuWhCr4y4O8HYYf00b0eilFIubiV0EekvIntEJE1EJpVR7hYRMSIS77kQa6Gml0OnQbDh/8HFM96ORimlADcSuoj4AW8BA4DOwCgR6VxCuQbABGCdp4Osla55FC6cdCR1pZSqBdy5Qu8OpBlj9htjsoFZwNASyj0HvAxc8GB8tVdYPLRLgu/fhOxz3o5GKaXcSuitgIMFljOc61xEJA5obYwpc843EblXRFJEJCUzM7PCwdY6102Es5mw6QNvR6KUUlW/KSoiNuBV4M/llTXGTDXGxBtj4kNDQ6t6au9rezW0vQbWvA45deMfE6VU7eVOQj8EtC6wHOZcl68B0AVYKSLpQAKw0OdvjOa7biJkHdF+6Uopr3MnoW8AIkUkQkQCgZHAwvyNxphTxphmxphwY0w4sBYYYoxJqZaIa5uIa6F1Aqx+FXLOezsapVQdVm5CN8bkAg8BS4FdwBxjzA4RmSIiQ6o7wFpPBK5/ErIOa48XpZRX+btTyBizBFhSZN0zpZTtXfWwLCbiWri8D6z+P8dDR8ENvR2RUqoO0idFPaXPM3D+V0fTi1JKeYEmdE9pGQPRI2Htf+DXdG9Ho5SqgzShe1KfZ0D84KvJ3o5EKZR1JkQAABBcSURBVFUHaUL3pIat4JpHYMenkPqlt6NRStUxmtA97ZpHoVkH+OwxyD7r7WiUUnWIJnRP8w+C378Op36CFf+ft6NRStUhmtCrQ9ur4ao/Om6QHtzg7WiUUnWEJvTqcuMUaNASFjyg47wopWqEJvTqEvw7GPIGHN8LS//m7WiUUnWAJvTq1L4PXD0eUqbD7jJHFlZKqSrThF7drn8aWnSFBQ/B6SPejkYp5cM0oVc3/0C4ZTrkXoBP7wN7nrcjUkr5KE3oNaFZJAx4GX78BpY/7+1olFI+ShN6TYm7E+LGwLevwo753o5GKeWDNKHXpIGvQFg3mP8A/LzT29EopXyMJvSa5B8Et86EoBCYdZtjuF2llPIQTeg17XctHEn9VAZ8co/eJFVKeYwmdG9o08PR/JL2Fax4wdvRKKV8hCZ0b4n/I0SPgDVvwMUsb0ejlPIBmtC9KfYOsOfA/pXejkQp5QM0oXtTmwQIagh7l3o7EqWUD9CE7k1+AdD+ekdC15ujSqkq8vd2AFaxeNtv47BkZl0kJf0XggP8aBoSSNsm9ck8cxGASwL9+OVsNuey82jdpB52A80bBOHvV/iz88zFXC7m5EHrvjTd8SkcXA9tE0s9f9aFHI6fyaZJ/UAa1gsA4Nez2VzMtXP09AW6hjVERLiQk8fp8zlc+rtgt+t28JdzhDWuh4i4vU/Gr+do0bAefjb39wHIsxuOnr5Aq0b1KrSfUqp8mtDd9ODHm1yvu73wVYX2veeaCJ4a3LnQuuv/uZJjWRcJIYAt9QLw37WozIR+9YvLybqYC0D6S4MAiH3ut3lLn7upC3cktGXcf1NYnXrcVaY863/8hVvf/Z5/DIvm1vjWbu1z8Jdz9PrHCh5Kas9f+nV0a598ryzdwzvf7OO7SdfTUpO6Uh7l000uW565kZSnbuDzCb28Gsfq1OPF1h3LclzRn+EStgbGwa5FYEypx8hP5qXZdODXUs9VltRjjh42m3866fY++bGv2VexcwF8m5YJwIkz2RXeVylVNp9O6I0uCaRZSBBXtPidt0Mp07rgno45SI9s8XYoSikL8+mEbhUpwQkgNp0EQylVJZrQa4Eztt9B6wTY+4W3Q1FKWZgm9NqiQz84+oNjjBellKoETei1RYd+ju+pX5ZdTimlSuFWQheR/iKyR0TSRGRSCdsfE5GdIrJNRL4WkbaeD9W6DKX3XnEWgNBO0LC1Y8CuypyjjB4yZe9XMAi396rUuQqfTynlaeUmdBHxA94CBgCdgVEi0rlIsc1AvDEmGpgH/MPTgfo8EWjfB/Z/A7napU8pVXHuXKF3B9KMMfuNMdnALGBowQLGmBXGmHPOxbVAmGfDtDahnKcp8ze3vwGysyBjfcXPUYGnPAvvVzQIt/aq1LkKn08p5WnuJPRWwMECyxnOdaW5G/i8pA0icq+IpIhISmZmpvtR1hUR14HNv9LNLkqpus2jN0VFZDQQD7xS0nZjzFRjTLwxJj40NNSTp/YNwc7ui5rQlVKV4E5CPwQUHOQjzLmuEBG5AXgSGGKMueiZ8OqgyBuc3ReL/YiVUqpM7iT0DUCkiESISCAwElhYsICIxALv4kjmxzwfZh3ScaDjuz5kpJSqoHITujEmF3gIWArsAuYYY3aIyBQRGeIs9goQAswVkS0isrCUw6nyNOsATdrBniXejkQpZTFuDZ9rjFkCLCmy7pkCr2/wcFx1l4jjKn39VLh4BoJCvB2RUsoi9EnR2qhDP8jLhv0rvB2JUspCNKHXRm0SHXON7tZmF6WU+zSh10Z+AdBpkGPSi5zz3o5GKWURmtBrq64jHU+N6hjpSik3aUKvrcJ7we/CYNtsb0eilLIITei1lc0G0bdC2teQddTb0SilLEATeg1wa/jcksSOBpMHG2eUfw4dPlepOk8Tem3W9HLHCIwp7xFIjrejUUrVcprQa4Dbw+eWJPFBOPMzN/t9W/YhdPhcpeo8Tei1XbskaNGV+/wWIdi9HY1SqhbThF4DKt2GDo5L2qvH0852lD62zaUfQtvQlarzNKFbQeebyDDNuM9/kbcjUUrVYprQa0CV2tAB/PyZmjuIbra9XCV7Sj6EtqErVedpQreIuXnXccpcwp/8dWRipVTJNKFbxHmCmZY7iBv8NkPGxnLLV7ZNXSllXZrQLeT9vP5kmoaw9IlacXexKq0n2vSilOdpQreQs9TjH7kj4OA62DzT2+FUoa9Lrfg8UsrnaEK3mHl510Kbq2HpU7SWn70djlKqFtGEbjEGGwx9E4yd6QH/JJiLJZfTK2Cl6hxN6FbU9HK4ZRodbId42P9Tr4WhbehK1S6a0K2q4wAW5F3Ng/4L6W3b4pUQtA1dqdpFE3oNqNKj/2WYlHMPO+xteTvgX4Sf3VapQ+qj/0r5Dk3oFnaeYMZkT+KIacK9GZPgyFZvh6SU8iJN6DWgyo/+l+E4Dbkj+wku2OpD8m005VTFYqvCo/+VCTv/fNqGrpTnaUL3AYcI5b2wF+Dccf4T+Dp+5Hk7JKWUF2hCrwHV1YZe0MHgSPj96/Sw7eZv/h9j7O6NnV6VNvTKhJ1/Pm1LV8rzNKH7kq4jeT+3H3f7f45tyWNg1yt1peoSf28HUBdUZxu66xDORum/597JOYJ4cNMMuHgKbp4K/oFl7FeZILQNXanaSK/QfY7wSu5I8m6YAjs+heQRcPGMt4NSStUATeg1oCba0IsOl2tPfBiGvgX7v4EZgyCr5HFftA1dKd+hCd2XxY6GkR9D5h54sxusfRvycr0dlVKqmriV0EWkv4jsEZE0EZlUwvYgEZnt3L5ORMI9HaiV1WQbejEd+8O45RB2FXwxCf7TA5Y8Dt+/BYc2EZRzkgByKxiEtqErVRuVe1NURPyAt4AbgQxgg4gsNMbsLFDsbuBXY0x7ERkJvAyMqI6AVSU07wyj/we7FsF3/4YtyZCdBcBwYHgwXNheHzLbQ3AjuKQpBFwCQQ0gJBQuaQZ+gWDzA7HR+MR5BthSCb/YAHafBHGsJyAY/IMBcSwLju+uZaFtbjoX5RxBv+6FwIaOzC7O6wqxOc5ZcDl/e5HjFF4n+gmhFCDlTVUmIonAZGNMP+fyEwDGmBcLlFnqLPO9iPgDR4FQU8bB4+PjTUpKigeq4J7wSYtr7Fwlibw0pNBy6rEzZW4vqmD5/LIlHSN/XftLQ8q8gm5mP05U3g6yT2cSwnkuk1/oEHySBiaLEHOWIHOREHOWSzhfXtVqBTuCQcgmkFz8Si1nSvuplPGBUNqbuNRjlbOtovuUfazS9vH0eSp2rPK3labixyszg5Xye61M3OVkygod6/hVj3DVoHvKPGKpZxLZaIyJL2mbO90WWwEHCyxnAD1KK2OMyRWRU0BT4HiRQO4F7gVo06aNW8F7ytJHrqXfv1YVWteheQh7fz7DP26J5mJuHk8v2MFDSe15c0VahY4d6GcjO6/0B3liWjeiZaPgQut+PZfD8TOOscy7hTcmtEFQmec4fuYiv57LIdDfRmTz4gm9Z/umNKwXgN0Y9mWepUPzsj8gIIQMwsnJM3y582d6RTbju+Dib4cA+0VC8k5iIw+bsSPYEWNnTWomUS1CaFbfD8Fgw06g/QJ+JteZWu2OC3RjxzGKu2PdybPZpB8/Q2zrRtjEjhjjKm8zeQSZC45lY3D8Ofy2XQy/HRe7q4wNx89esBNkv+BaLqSMCxcpJ/2VvE/pSj9eGTGUuqkycVc8XVbqZ1DJO9ulncvTv4fKxV0zv+/AkCZlHLHyarQfujFmKjAVHFfoNXnujpc1IP2lQWWWuSMxHIC/9OtYAxEppZRnuXNT9BDQusBymHNdiWWcTS4NgROeCFAppZR73EnoG4BIEYkQkUBgJLCwSJmFwBjn62HA8rLaz5VSSnleuU0uzjbxh4ClgB/wnjFmh4hMAVKMMQuB6cBMEUkDfsGR9JVSStUgt9rQjTFLgCVF1j1T4PUFHD3glFJKeYk+KaqUUj5CE7pSSvkITehKKeUjNKErpZSPKPfR/2o7sUgmcKCSuzejyFOoFqZ1qX18pR6gdamtqlKXtsaY0JI2eC2hV4WIpJQ2loHVaF1qH1+pB2hdaqvqqos2uSillI/QhK6UUj7Cqgl9qrcD8CCtS+3jK/UArUttVS11sWQbulJKqeKseoWulFKqCE3oSinlIyyX0MubsLo2EJH3ROSYiGwvsK6JiHwpIqnO742d60VE3nDWZ5uIxBXYZ4yzfKqIjCnpXNVcj9YiskJEdorIDhGZYOG6BIvIehHZ6qzL353rI5wTm6c5JzoPdK4vdeJzEXnCuX6PiPSr6bo4Y/ATkc0i8pnF65EuIj+IyBYRSXGus9z7yxlDIxGZJyK7RWSXiCTWeF2MMZb5wjF87z6gHRAIbAU6ezuuEuK8FogDthdY9w9gkvP1JOBl5+uBwOc4ZrlKANY51zcB9ju/N3a+blzD9WgBxDlfNwD2Ap0tWhcBQpyvA4B1zhjnACOd698B/uR8/QDwjvP1SGC283Vn5/suCIhwvh/9vPAeewz4GPjMuWzVeqQDzYqss9z7yxnHB8A9zteBQKOarkuNVtgDP7BEYGmB5SeAJ7wdVymxhlM4oe8BWjhftwD2OF+/C4wqWg4YBbxbYH2hcl6q0wLgRqvXBbgE2IRjbtzjgH/R9xeO8f8Tna/9neWk6HuuYLkajD8M+Bq4HvjMGZfl6uE8bzrFE7rl3l84Zmn7EWdHE2/VxWpNLiVNWN3KS7FUVHNjzBHn66NAc+fr0upUq+rq/Fc9FseVrSXr4mym2AIcA77EcVV60hiTW0JchSY+B/InPq8NdfkXMBFcs2E3xZr1AMdMystEZKM4JpEHa76/IoBM4H1nU9j/E5H61HBdrJbQfYJxfPRapr+oiIQAnwCPGGNOF9xmpboYY/KMMTE4rnC7A528HFKFichg4JgxZqO3Y/GQa4wxccAA4EERubbgRgu9v/xxNLO+bYyJBc7iaGJxqYm6WC2huzNhdW31s4i0AHB+P+ZcX1qdakVdRSQARzL/yBjzP+dqS9YlnzHmJLACR9NEI3FMbF40rtImPvd2XXoCQ0QkHZiFo9nldaxXDwCMMYec348Bn+L4oLXi+ysDyDDGrHMuz8OR4Gu0LlZL6O5MWF1bFZxIewyO9uj89Xc673onAKec/6ItBfqKSGPnnfG+znU1RkQEx3yxu4wxrxbYZMW6hIpII+frejjuBezCkdiHOYsVrUtJE58vBEY6e49EAJHA+pqpBRhjnjDGhBljwnG8/5cbY27HYvUAEJH6ItIg/zWO98V2LPj+MsYcBQ6KSEfnqj7ATmq6LjV9E8QDNx8G4uhtsQ940tvxlBJjMnAEyMHxyX03jnbLr4FU4CugibOsAG856/MDEF/gOHcBac6vP3qhHtfg+BdxG7DF+TXQonWJBjY767IdeMa5vh2ORJYGzAWCnOuDnctpzu3tChzrSWcd9wADvPg+681vvVwsVw9nzFudXzvy/56t+P5yxhADpDjfY/Nx9FKp0broo/9KKeUjrNbkopRSqhSa0JVSykdoQldKKR+hCV0ppXyEJnSllPIRmtCVUspHaEJXSikf8f8DnTMPPFe6RooAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train:\n",
    "    for m, n in hamming_sizes:\n",
    "        print(\"Training supervised (%d, %d)\" % (m, n))\n",
    "        # Training loop\n",
    "        encoder, decoder, errors = train_supervised(m=m, n=n, snr_db=snr_db, chann_type=chann_type, batch_size=32,\n",
    "                                                    n_epochs=10000, lr=0.001, plot=1000)\n",
    "\n",
    "        # Save the trained models\n",
    "        enc_filename = \"%s/%s_%d_%d_encoder.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "        dec_filename = \"%s/%s_%d_%d_decoder.pth\" % (MODELS_FOLDER, chann_type, m, n)\n",
    "        torch.save(encoder.state_dict(), enc_filename)\n",
    "        torch.save(decoder.state_dict(), dec_filename)\n",
    "\n",
    "        print(\"Models saved in %s, %s\" % (enc_filename, dec_filename))\n",
    "        \n",
    "        torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x2160 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observe the constellations that were generated with each approach\n",
    "# An embedding to 2 dimensions using t-SNE was used to plot this\n",
    "hamming_sizes = [(16, 4), (16, 7), (256, 8), (4, 2)]\n",
    "fig, axs = plt.subplots(4, 2, figsize=(18, 30), facecolor='w')\n",
    "for i, (m, n) in enumerate(hamming_sizes):\n",
    "    axs[i, 0] = plot_constellation(m=m, n=n, model=\"supervised\", ax=axs[i, 0], device=device)\n",
    "    axs[i, 1] = plot_constellation(m=m, n=n, model=\"alternated\", ax=axs[i, 1], device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BPSK communication\n",
    "\n",
    "We can also simulate BPSK modulation and then compare with the results we obtained. As encoder we would use a Hamming encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bpsk_communication(m, n, snr_db, n_blocks, chann_type=chann_type, verbose=False):\n",
    "    # Get k. Number of bits necessary to transmit the m messages\n",
    "    k = int(math.log2(m))\n",
    "\n",
    "    # Generate inputs\n",
    "    x = np.random.randint(0, 2, size=(n_blocks, k))\n",
    "    if verbose: print(\"Original x\")\n",
    "    if verbose: print(x)\n",
    "    \n",
    "    # Encode the messages using Hamming\n",
    "    # Strange output from library sk_dsp_comm.fec_block when doing hamming coding\n",
    "    # e.g. (7,4) hamming code object\n",
    "    # Supressing such output with this line\n",
    "    with io.capture_output() as captured:\n",
    "        x_encoded = block_encoder(x, n, k)\n",
    "    if verbose: print(\"Encoded x\")\n",
    "    if verbose: print(x_encoded)\n",
    "    \n",
    "    # Signal vector to transmit (modulation)\n",
    "    s_transmit = 2*x_encoded - 1\n",
    "    if verbose: print(\"Modulated x\")\n",
    "    if verbose: print(s_transmit)\n",
    "    \n",
    "    # Add the noise\n",
    "    s_noise = channel(s_transmit, n, k, snr_db, chann_type=chann_type)\n",
    "    if verbose: print(\"x with noise after channel\")\n",
    "    if verbose: print(s_noise)\n",
    "    \n",
    "    # Demoludation of the signal\n",
    "    y = np.sign(s_noise)\n",
    "    y_enc = (y + 1)/2\n",
    "    \n",
    "    if verbose: print(\"Demodulated received x\")\n",
    "    if verbose: print(y_enc.astype(int))\n",
    "\n",
    "    # Decode them using Hamming\n",
    "    with io.capture_output() as captured:\n",
    "        x_rec = block_decoder(y_enc.astype(int), n, k)\n",
    "    \n",
    "    if verbose: print(\"Decoded x\")\n",
    "    if verbose: print(x_rec)\n",
    "\n",
    "    # Get the bler\n",
    "    block_bler = bler(x, x_rec)\n",
    "    if verbose: print(\"BLER\")\n",
    "    if verbose: print(block_bler)\n",
    "    \n",
    "    print(\"Finished calculations for BPSK(%d, %d) channel: %s. SNR dB: %f.\" % (n, k, chann_type, snr_db))\n",
    "\n",
    "    return block_bler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original x\n",
      "[[0 1 0 1]\n",
      " [1 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 1 0]\n",
      " [0 1 0 0]\n",
      " [1 0 1 0]\n",
      " [0 0 0 1]\n",
      " [0 0 0 1]\n",
      " [1 1 1 0]\n",
      " [1 0 1 0]]\n",
      "Encoded x\n",
      "[[0 1 0 1 0 1 1]\n",
      " [1 0 0 0 1 1 1]\n",
      " [0 0 0 0 0 0 0]\n",
      " [0 0 1 0 0 1 1]\n",
      " [0 1 0 0 1 1 0]\n",
      " [1 0 1 0 1 0 0]\n",
      " [0 0 0 1 1 0 1]\n",
      " [0 0 0 1 1 0 1]\n",
      " [1 1 1 0 0 1 0]\n",
      " [1 0 1 0 1 0 0]]\n",
      "Modulated x\n",
      "[[-1  1 -1  1 -1  1  1]\n",
      " [ 1 -1 -1 -1  1  1  1]\n",
      " [-1 -1 -1 -1 -1 -1 -1]\n",
      " [-1 -1  1 -1 -1  1  1]\n",
      " [-1  1 -1 -1  1  1 -1]\n",
      " [ 1 -1  1 -1  1 -1 -1]\n",
      " [-1 -1 -1  1  1 -1  1]\n",
      " [-1 -1 -1  1  1 -1  1]\n",
      " [ 1  1  1 -1 -1  1 -1]\n",
      " [ 1 -1  1 -1  1 -1 -1]]\n",
      "x with noise after channel\n",
      "[[-8.14134404e-01  1.84995920e+00 -1.34902378e+00  6.71316228e-01\n",
      "  -7.26406915e-01  2.05747450e-02  2.29388798e+00]\n",
      " [ 7.94546866e-01 -1.20709123e+00 -1.14982768e+00 -7.13905190e-01\n",
      "   1.21538198e+00  1.94769931e+00  1.89578371e+00]\n",
      " [-5.35557069e-01 -6.73369245e-01 -1.00612485e+00 -1.59284200e+00\n",
      "  -6.42535284e-01 -1.61166361e+00 -1.10165551e+00]\n",
      " [-2.37519342e-01 -1.26340094e+00  5.22430666e-01 -3.02752728e-01\n",
      "  -1.11510271e+00 -2.03402577e+00  1.12809728e+00]\n",
      " [ 1.22503001e-01  2.52709566e+00 -3.22018409e-01 -6.12962231e-01\n",
      "   1.84169570e+00  6.54052916e-01 -2.60252957e+00]\n",
      " [ 1.51049954e+00 -2.24062918e+00 -1.25072365e+00 -1.97446563e+00\n",
      "   1.78585131e+00 -6.27813211e-01 -1.19728793e+00]\n",
      " [-7.11847109e-01 -2.99242750e+00 -3.33302415e-02  2.01371052e-01\n",
      "   8.26979498e-01 -4.58934508e-01  1.06267526e+00]\n",
      " [-1.12825824e+00 -1.25327761e+00 -4.90054751e-01  5.54642582e-01\n",
      "   1.13412143e+00 -2.12692010e+00  5.36191857e-01]\n",
      " [ 2.74106711e+00  7.73461902e-01  4.99469585e-01 -1.57747963e+00\n",
      "  -5.82560353e-02  2.14572659e+00 -3.76999681e+00]\n",
      " [ 6.27996048e-01 -7.17794686e-01  1.61153386e-01 -9.28562775e-01\n",
      "   2.21422592e+00 -2.16973736e-01  2.81167618e-03]]\n",
      "Demodulated received x\n",
      "[[0 1 0 1 0 1 1]\n",
      " [1 0 0 0 1 1 1]\n",
      " [0 0 0 0 0 0 0]\n",
      " [0 0 1 0 0 0 1]\n",
      " [1 1 0 0 1 1 0]\n",
      " [1 0 0 0 1 0 0]\n",
      " [0 0 0 1 1 0 1]\n",
      " [0 0 0 1 1 0 1]\n",
      " [1 1 1 0 0 1 0]\n",
      " [1 0 1 0 1 0 1]]\n",
      "Decoded x\n",
      "[[0 1 0 1]\n",
      " [1 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 1 0]\n",
      " [0 1 0 0]\n",
      " [1 0 1 0]\n",
      " [0 0 0 1]\n",
      " [0 0 0 1]\n",
      " [1 1 1 0]\n",
      " [1 0 1 0]]\n",
      "BLER\n",
      "0.0\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 0.000000.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize BPSK communication\n",
    "bpsk_communication(m=16, n=7, snr_db=0, chann_type=\"AWGN\", n_blocks=10, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before watching the performance of the systems at different noise levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for BPSK(4, 4) channel: AWGN. SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for BPSK(7, 4) channel: AWGN. SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for BPSK(8, 8) channel: AWGN. SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -4.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: -4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -3.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: -3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -2.000000.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: -2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: -1.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: -1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 0.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 0.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 1.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 1.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 2.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 2.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 3.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 3.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 4.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 4.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 5.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 5.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 6.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 6.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 7.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 7.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 8.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 8.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for channel (AWGN). SNR dB: 9.000000.\n",
      "Finished calculations for BPSK(2, 2) channel: AWGN. SNR dB: 9.000000.\n"
     ]
    }
   ],
   "source": [
    "# Defining the hamming encoding sizes (m, n)\n",
    "hamming_sizes = [(16, 4), (16, 7), (256, 8), (4, 2)]\n",
    "\n",
    "# Defining all noise levels\n",
    "snrs_db = np.arange(-4, 10, 1)\n",
    "\n",
    "# Number of input messages to simulate\n",
    "n_blocks = 300000\n",
    "\n",
    "# To store the resutls\n",
    "results_y = np.zeros((3, len(hamming_sizes), len(snrs_db)))\n",
    "\n",
    "# Iterate over all the (m, n) encodings\n",
    "for hamm_n, (m, n) in enumerate(hamming_sizes):\n",
    "    # Iterate over all the noise levels\n",
    "    for snr_n, snr_db in enumerate(snrs_db):\n",
    "        # Get the bler at each noise level\n",
    "        bler_supervised = nn_communication(m=m, n=n, snr_db=snr_db, n_blocks=n_blocks, model=\"supervised\", chann_type=\"AWGN\")\n",
    "        bler_alternated = nn_communication(m=m, n=n, snr_db=snr_db, n_blocks=n_blocks, model=\"alternated\", chann_type=\"AWGN\")\n",
    "        bler_bpsk = bpsk_communication(m=m, n=n, snr_db=snr_db, n_blocks=n_blocks, chann_type=\"AWGN\")\n",
    "\n",
    "        # Store the results\n",
    "        results_y[0, hamm_n, snr_n] = bler_supervised\n",
    "        results_y[1, hamm_n, snr_n] = bler_alternated\n",
    "        results_y[2, hamm_n, snr_n] = bler_bpsk\n",
    "        \n",
    "        torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x792 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing each autoencoder with the theoritcal result\n",
    "fig, axs = plt.subplots(2, 2, figsize=(13, 11), facecolor='w')\n",
    "\n",
    "axs[0, 0].semilogy(snrs_db, results_y[0, 0,:], label='Supervised')\n",
    "axs[0, 0].semilogy(snrs_db, results_y[1, 0,:], label='Alternated')\n",
    "axs[0, 0].semilogy(snrs_db, results_y[2, 0,:], label='BPSK')\n",
    "axs[0, 0].legend(loc='upper right')\n",
    "axs[0, 0].set_title('Encoding (m, n)=(16, 4) (n, k)=(4, 4)')\n",
    "\n",
    "axs[0, 1].semilogy(snrs_db, results_y[0, 1,:], label='Supervised')\n",
    "axs[0, 1].semilogy(snrs_db, results_y[1, 1,:], label='Alternated')\n",
    "axs[0, 1].semilogy(snrs_db, results_y[2, 1,:], label='BPSK')\n",
    "axs[0, 1].legend(loc='upper right')\n",
    "axs[0, 1].set_title('Encoding (m, n)=(16, 7) (n, k)=(7, 4)')\n",
    "\n",
    "axs[1, 0].semilogy(snrs_db, results_y[0, 2,:], label='Supervised')\n",
    "axs[1, 0].semilogy(snrs_db, results_y[1, 2,:], label='Alternated')\n",
    "axs[1, 0].semilogy(snrs_db, results_y[2, 2,:], label='BPSK')\n",
    "axs[1, 0].legend(loc='upper right')\n",
    "axs[1, 0].set_title('Encoding (m, n)=(256, 8) (n, k)=(8, 8)')\n",
    "\n",
    "axs[1, 1].semilogy(snrs_db, results_y[0, 3,:], label='Supervised')\n",
    "axs[1, 1].semilogy(snrs_db, results_y[1, 3,:], label='Alternated')\n",
    "axs[1, 1].semilogy(snrs_db, results_y[2, 3,:], label='BPSK')\n",
    "axs[1, 1].legend(loc='upper right')\n",
    "axs[1, 1].set_title('Encoding (m, n)=(4, 2) (n, k)=(2, 2)')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.grid(True)\n",
    "    ax.set(xlabel='Eb/No (dB)', ylabel='BLER')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
